how to write author names in research paper

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How to Order and Format Author Names in Scientific Papers

As the world becomes more interconnected, the production of knowledge increasingly relies on collaboration. Scientific papers, the primary medium through which researchers communicate their findings, often feature multiple authors. However, authorship isn't merely a reflection of those who contributed to a study but often denotes prestige, recognition, and responsibility. In academic papers, the order of authors is not arbitrary. It can symbolize the level of contribution and the role played by each author in the research process. Deciding on the author order can sometimes be a complex and sensitive issue, making it crucial to understand the different roles and conventions of authorship in scientific research. This article will explore the various types of authors found in scientific papers, guide you on how to correctly order and format author names, and offer insights to help you navigate this critical aspect of academic publishing.

The first author

The first author listed in a scientific paper is typically the person who has made the most substantial intellectual contribution to the work. This role is often filled by a junior researcher such as a Ph.D. student or postdoctoral fellow, who has been intimately involved in almost every aspect of the project.

The first author usually plays a pivotal role in designing and implementing the research, including the formation of hypotheses, experimental design, data collection, data analysis, and interpretation of the findings. They also commonly take the lead in manuscript preparation, writing substantial portions of the paper, including the often-challenging task of turning raw data into a compelling narrative.

In academia, first authorship is a significant achievement, a clear demonstration of a researcher's capabilities and dedication. It indicates that the researcher possesses the skills and tenacity to carry a project from inception to completion. This position can dramatically impact a researcher's career trajectory, playing a critical role in evaluations for promotions, grants, and future academic positions.

However, being the first author is not just about prestige or professional advancement. It carries a weight of responsibility. The first author is generally expected to ensure the integrity and accuracy of the data presented in the paper. They are often the person who responds to reviewers' comments during the peer-review process and makes necessary revisions to the manuscript.

Also, as the first author, it is typically their duty to address any questions or critiques that may arise post-publication, often having to defend the work publicly, even years after publication.

Thus, first authorship is a role that offers significant rewards but also requires a strong commitment to uphold the principles of scientific integrity and transparency. While it's a coveted position that can be a steppingstone to career progression, the associated responsibilities and expectations mean that it should not be undertaken lightly.

The middle authors

The middle authors listed on a scientific paper occupy an essential, albeit sometimes ambiguous, role in the research project. They are typically those who have made significant contributions to the project, but not to the extent of the first author. This group often includes a mix of junior and senior researchers who have provided key input, assistance, or resources to the project.

The roles of middle authors can be quite diverse. Some might be involved in specific aspects of data collection or analysis. Others may bring specialized knowledge or technical skills essential to the project, providing expertise in a particular methodology, statistical analysis, or experimental technique. There might also be middle authors who have contributed vital resources to the project, such as unique reagents or access to a particular patient population.

In some fields, the order of middle authors reflects the degree of their contribution. The closer a middle author is to the first position, the greater their involvement, with the second author often having made the next largest contribution after the first author. This order may be negotiated among the authors, requiring clear communication and consensus.

However, in other disciplines, particularly those where large collaborative projects are common, the order of middle authors may not necessarily reflect their level of contribution. In such cases, authors might be listed alphabetically, or by some other agreed-upon convention. Therefore, it's crucial to be aware of the norms in your specific field when deciding the order of middle authors.

Being a middle author in a scientific paper carries less prestige and responsibility than being a first or last author, but it is by no means a minor role. Middle authors play a crucial part in the scientific endeavor, contributing essential expertise and resources. They are integral members of the research team whose collective efforts underpin the progress and achievements of the project. Without their diverse contributions, the scope and impact of scientific research would be significantly diminished.

The last author

In the listing of authors on a scientific paper, the final position carries a unique significance. It is typically occupied by the senior researcher, often the head of the laboratory or the principal investigator who has supervised the project. While they might not be involved in the day-to-day aspects of the work, they provide overarching guidance, mentorship, and often the resources necessary for the project's fruition.

The last author's role is multidimensional, often balancing the responsibilities of project management, funding acquisition, and mentorship. They guide the research's direction, help troubleshoot problems, and provide intellectual input to the project's design and interpretation of results. Additionally, they usually play a key role in the drafting and revision of the manuscript, providing critical feedback and shaping the narrative.

In academia, the last author position is a symbol of leadership and scientific maturity. It indicates that the researcher has progressed from being a hands-on contributor to someone who can guide a team, secure funding, and deliver significant research projects. Being the last author can have substantial implications for a researcher's career, signaling their ability to oversee successful projects and mentor the next generation of scientists.

However, along with prestige comes significant responsibility. The last author is often seen as the guarantor of the work. They are held accountable for the overall integrity of the study, and in cases where errors or issues arise, they are expected to take the lead in addressing them.

The convention of the last author as the senior researcher is common in many scientific disciplines, especially in the life and biomedical sciences. However, it's important to note that this is not a universal standard. In some fields, authors may be listed purely in the order of contribution or alphabetically. Therefore, an understanding of the specific norms and expectations of your scientific field is essential when considering author order.

In sum, the position of the last author, much like that of the first author, holds both honor and responsibility, reflecting a leadership role that goes beyond mere intellectual contribution to include mentorship, management, and accountability.

Formatting author names

When it comes to scientific publishing, details matter, and one such detail is the correct formatting of author names. While it may seem like a minor concern compared to the intellectual challenges of research, the proper formatting of author names is crucial for several reasons. It ensures correct attribution of work, facilitates accurate citation, and helps avoid confusion among researchers in the same field. This section will delve deeper into the conventions for formatting author names, offering guidance to ensure clarity and consistency in your scientific papers.

Typically, each author's full first name, middle initial(s), and last name are listed. It's crucial that the author's name is presented consistently across all their publications to ensure their work is correctly attributed and easily discoverable.

Here is a basic example following a common convention:

• Standard convention: John D. Smith

However, conventions can vary depending on cultural naming practices. In many Western cultures, the first name is the given name, followed by the middle initial(s), and then the family name. On the other hand, in many East Asian cultures, the family name is listed first.

Here is an example following this convention:

• Asian convention: Wang Xiao Long

When there are multiple authors, their names are separated by commas. The word "and" usually precedes the final author's name.

Here's how this would look:

• John D. Smith, Jane A. Doe, and Richard K. Jones

However, author name formatting can differ among journals. Some may require initials instead of full first names, or they might have specific guidelines for handling hyphenated surnames or surnames with particles (e.g., "de," "van," "bin"). Therefore, it's always important to check the specific submission guidelines of the journal to which you're submitting your paper.

Moreover, the formatting should respect each author's preferred presentation of their name, especially if it deviates from conventional Western naming patterns. As the scientific community becomes increasingly diverse and global, it's essential to ensure that each author's identity is accurately represented.

In conclusion, the proper formatting of author names is a vital detail in scientific publishing, ensuring correct attribution and respect for each author's identity. It may seem a minor point in the grand scheme of a research project, but getting it right is an essential part of good academic practice.

The concept of authorship in scientific papers goes well beyond just listing the names of those involved in a research project. It carries critical implications for recognition, responsibility, and career progression, reflecting a complex nexus of contribution, collaboration, and intellectual leadership. Understanding the different roles, correctly ordering the authors, and appropriately formatting the names are essential elements of academic practice that ensure the rightful attribution of credit and uphold the integrity of scientific research.

Navigating the terrain of authorship involves managing both objective and subjective elements, spanning from the universally acknowledged conventions to the nuances particular to different scientific disciplines. Whether it's acknowledging the pivotal role of the first author who carried the project from the ground up, recognizing the valuable contributions of middle authors who provided key expertise, or highlighting the mentorship and leadership role of the last author, each position is an integral piece in the mosaic of scientific authorship.

Furthermore, beyond the order of authors, the meticulous task of correctly formatting the author names should not be underestimated. This practice is an exercise in precision, respect for individual identity, and acknowledgement of cultural diversity, reflecting the global and inclusive nature of contemporary scientific research.

As scientific exploration continues to move forward as a collective endeavor, clear and equitable authorship practices will remain crucial. These practices serve not only to ensure that credit is assigned where it's due but also to foster an environment of respect and transparency. Therefore, each member of the scientific community, from fledgling researchers to seasoned scientists, would do well to master the art and science of authorship in academic publishing. After all, it is through this collective recognition and collaboration that we continue to expand the frontiers of knowledge.

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How to Order Authors in Scientific Papers

It’s rare that an article is authored by only one or two people anymore. In fact, the average original research paper has five authors these days. The growing list of collaborative research projects raises important questions regarding the author order for research manuscripts and the impact an author list has on readers’ perceptions.

With a handful of authors, a group might be inclined to create an author name list based on the amount of work contributed. What happens, though, when you have a long list of authors? It would be impractical to rank the authors by their relative contributions. Additionally, what if the authors contribute relatively equal amounts of work? Similarly, if a study was interdisciplinary (and many are these days), how can one individual’s contribution be deemed more significant than another’s?

Why does author order matter?

Although an author list should only reflect those who have made substantial contributions to a research project and its draft manuscript (see, for example, the authorship guidelines of the International Committee of Medical Journal Editors ), we’d be remiss to say that author order doesn’t matter. In theory, everyone on the list should be credited equally since it takes a team to successfully complete a project; however, due to industry customs and other practical limitations, some authors will always be more visible than others.

The following are some notable implications regarding author order.

• The “first author” is a coveted position because of its increased visibility. This author is the first name readers will see, and because of various citation rules, publications are usually referred to by the name of the first author only. In-text or bibliographic referencing rules, for example, often reduce all other named authors to “et al.” Since employers use first-authorship to evaluate academic personnel for employment, promotion, and tenure, and since graduate students often need a number of first-author publications to earn their degree, being the lead author on a manuscript is crucial for many researchers, especially early in their career.
• The last author position is traditionally reserved for the supervisor or principal investigator. As such, this person receives much of the credit when the research goes well and the flak when things go wrong. The last author may also be the corresponding author, the person who is the primary contact for journal editors (the first author could, however, fill this role as well, especially if they contributed most to the work).
• Given that there is no uniform rule about author order, readers may find it difficult to assess the nature of an author’s contribution to a research project. To address this issue, some journals, particularly medical ones, nowadays insist on detailed author contribution notes (make sure you check the target journal guidelines before submission to find out how the journal you are planning to submit to handles this). Nevertheless, even this does little to counter how strongly citation rules have enhanced the attention first-named authors receive.

Common Methods for Listing Authors

The following are some common methods for establishing author order lists.

• Relative contribution. As mentioned above, the most common way authors are listed is by relative contribution. The author who made the most substantial contribution to the work described in an article and did most of the underlying research should be listed as the first author. The others are ranked in descending order of contribution. However, in many disciplines, such as the life sciences, the last author in a group is the principal investigator or “senior author”—the person who often provides ideas based on their earlier research and supervised the current work.
• Alphabetical list . Certain fields, particularly those involving large group projects, employ other methods . For example, high-energy particle physics teams list authors alphabetically.
• Multiple “first” authors . Additional “first” authors (so-called “co-first authors”) can be noted by an asterisk or other symbols accompanied by an explanatory note. This practice is common in interdisciplinary studies; however, as we explained above, the first name listed on a paper will still enjoy more visibility than any other “first” author.
• Multiple “last” authors . Similar to recognizing several first authors, multiple last authors can be recognized via typographical symbols and footnotes. This practice arose as some journals wanted to increase accountability by requiring senior lab members to review all data and interpretations produced in their labs instead of being awarded automatic last-authorship on every publication by someone in their group.
• Negotiated order . If you were thinking you could avoid politics by drowning yourself in research, you’re sorely mistaken. While there are relatively clear guidelines and practices for designating first and last authors, there’s no overriding convention for the middle authors. The list can be decided by negotiation, so sharpen those persuasive argument skills!

As you can see, choosing the right author order can be quite complicated. Therefore, we urge researchers to consider these factors early in the research process and to confirm this order during the English proofreading process, whether you self-edit or received manuscript editing or paper editing services , all of which should be done before submission to a journal. Don’t wait until the manuscript is drafted before you decide on the author order in your paper. All the parties involved will need to agree on the author list before submission, and no one will want to delay submission because of a disagreement about who should be included on the author list, and in what order (along with other journal manuscript authorship issues).

On top of that, journals sometimes have clear rules about changing authors or even authorship order during the review process, might not encourage it, and might require detailed statements explaining the specific contribution of every new/old author, official statements of agreement of all authors, and/or a corrigendum to be submitted, all of which can further delay the publication process. We recommend periodically revisiting the named author issue during the drafting stage to make sure that everyone is on the same page and that the list is updated to appropriately reflect changes in team composition or contributions to a research project.

How to Handle Author Names in APA Style

The APA Style uses a basic author-date citation style, where cited references are then listed in an APA Style Reference List, but what happens when the names get complicated? Professional titles like Captain, authors with only one name, and authors who change their names—all of these can cause confusion to those using the APA Style. Luckily, in APA format, there are solutions to all of these questions and more. Read on to learn what to do in the following common sticky situations when:

• Citing authors with Professional titles or academic credentials like Dr. or Reverend or Captain
• Citing authors with Single name
• Citing authors with Name changes
• Citing authors with Multiple part surnames

Below, you’ll find APA Style examples for all of these, including APA Citations and APA References.

Related: Do you have questions on manuscript drafting? Get personalized answers on the FREE Q&A Forum!

Professional Title

When authors have professional titles referring to their degrees, like Dr. or M.A., these academic credentials are never included in citing the author’s name. But what about other kinds of titles, like Captain or Reverend?

According to Chelsea Lee at the APA Style Blog, the current APA Style guide specifies that almost all professional titles should be left out. Do not include Captain, Reverend, Professor, Honorable, Vice President, or any other business title when writing research papers in APA Style. There are two important exceptions to this rule. First, titles are included for religious leaders like the Pope. Second, titles are included for nobility. Here are some examples:

APA Style Religious Leader Reference

Pope Francis. (2013). Lumen fidei [The light of faith] [Encyclical letter]. Retrieved from http://w2.vatican.va/content/francesco/en/encyclicals/documents/papa-francesco_20130629_enciclica-lumen-fidei.html

APA Style Religious Leader In-Text Citation

(Pope Francis, 2013)

APA Style Nobility Reference

The Prince of Wales (with Juniper, T., & Skelly, I). (2010). Harmony: A new way of looking at our world . New York, NY: HarperCollins.

APA Style Nobility In-Text Citation

(The Prince of Wales, 2010)

Notice that, in the in-text citation for nobility, the article, “The” is included. This differs from many APA Style Citations.

Only One Name

When an author has only one name, how do you cite the Author’s Name? How do you alphabetize an author with only one name? This is an easy one. Always include the full name of the author in both the in-text citation and the reference. For example:

  APA Style One Name Author Reference

Mishawaka. (1965). Mishawaka: An autobiography . Thousand Oaks, CA: Sage.

APA Style One Name Author In-Text Citation

(Mishawaka, 1965)

Name Changes

There may be many reasons for authors to change their names. They may change their surnames after marriage, or take on a hyphenated surname. If they get divorced, they may drop the hyphenation or change back to their original names. Occasionally, an author will inconsistently publish using a middle initial: sometimes John Author, sometimes John Q. Author. What does the APA Style guide require in these cases?

In most instances, it is not necessary to make a note of an author’s name change. If you are using two sources by the same author using different names, simply cite and reference both works normally, using the two different names. If the author’s initials did not change but his or her first name did, you will need to specify the different first names in the reference list, like this:

Author, J. [John] Q. (2017). Title of book … Author, J. [Jane] Q. (2010). Title of book …

If it is useful to the reader and relevant to your method of writing research papers, you may choose to make a note of the name change within the text. For example:

John Author (previously Jane Author; 2016), wrote that life can be difficult for transgendered researchers.

Otherwise, no explicit mention of the name change is required.

Multipart Last Names

Multipart surnames are one of the most confusing name variations in the APA Style Citations. Authors may have surnames consisting of more than one word, or they may have particles preceding their last names like “von” or “van”, which appear as separate words. As if that weren’t enough, some authors may have suffixes like “Jr.” after their surnames.

Mistakes when citing and referencing multipart last names in APA Style are very common. It’s important to know the rules for each of these cases. Luckily, there are no serious complications. Here is a summary of the APA Referencing Style requirements for each of the cases mentioned:

• For surnames with more than one work (e.g., Gonzalez Gutierrez) include both names in the reference list and the in-text citation. Alphabetize using the first word of the surname.
• For particles like von and van, include them in both the reference list and the in-text citation. Use them to alphabetize. For example, van Horne should be listed in the references under V, not under H.
• For suffixes like Jr., include them in the reference list, but do not include them in the in-text citation.

Authors with Two-part Surnames

• When surnames are hyphenated, make sure to include both names along with the hyphen in the reference list and the in-text citation.
• When surnames have two parts that is separated by a space but no hyphen, include both in the reference list and the in-text citation. E.g.: Most Spanish names use this format.

More Useful Resources

If your question about APA Referencing was answered in this post, please comment to let us know! If not, there are many other sources of information on Writing Author Names in APA Style. Here are a few resources that you might find useful:

• How to Capitalize Author Names in APA Style: http://blog.apastyle.org/apastyle/2012/02/how-to-capitalize-author-names-in-apa-style.html
• APA Citation Style Guide to Author Names, including information on Citing Six or More Authors: http://research.moreheadstate.edu/c.php?g=107001&p=695197
• How to Reference an Author or Authors in APA Format: https://www.verywell.com/how-to-reference-an-author-or-authors-in-apa-format-2794855
• Chelsea Lee (2017, May 31) What’s in a Name? Names With Titles in Them . Retrieved from http://blog.apastyle.org/apastyle/2017/05/whats-in-a-name-names-with-titles-in-them.html
• Chelsea Lee (2017, May 24) What’s in a Name? Authors With Only One Name. Retrieved from http://blog.apastyle.org/apastyle/2017/05/whats-in-a-name-authors-with-only-one-name.html
• Chelsea Lee (2017, May 10) What’s in a Name? Inconsistent Formats and Name Changes . Retrieved from http://blog.apastyle.org/apastyle/2017/05/whats-in-a-name-inconsistent-formats-and-name-changes.html
• Chelsea Lee (2017, May 4) What’s in a Name? Two-Part Surnames in APA Style . Retrieved from http://blog.apastyle.org/apastyle/2017/05/whats-in-a-name-two-part-surnames-in-apa-style.html

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10 Easy Steps: How to Write Author Name in Research Paper

Step 1: Understand the Importance of Citing Author Names

When writing a research paper , it is crucial to give credit to the authors whose work you have referenced. Properly citing author names not only acknowledges their contribution but also adds credibility to your own work. It allows readers to locate the original source and verify the information you have presented. In this article, we will guide you through ten easy steps on how to write author names in your research paper.

Why is citing author names important?

Citing author names is important because it:

• Shows respect for the original authors
• Allows readers to find the original source
• Provides evidence for your claims
• Prevents plagiarism

Step 2: Understand the Different Citation Styles

Before you start citing author names, it is essential to familiarize yourself with the different citation styles commonly used in academic writing . The most popular citation styles include APA (American Psychological Association), MLA (Modern Language Association), and Chicago/Turabian. Each style has its own set of rules for formatting author names, so make sure to consult the appropriate style guide for your research paper.

What are the main citation styles?

The main citation styles used in academic writing are:

• APA (American Psychological Association)
• MLA (Modern Language Association)
• Chicago/Turabian

Step 3: Determine the Number of Authors

The way you write author names in your research paper depends on the number of authors involved. If there is only one author, the format will be different compared to multiple authors. It is important to correctly identify the number of authors to ensure accurate citation.

How do you determine the number of authors?

To determine the number of authors, check the original source of the research paper. Look for the author information provided, such as the author's name, affiliations, and any additional contributors.

Step 4: Format Author Names for Single Authors

If there is only one author, the format for citing their name will be different compared to multiple authors. In most citation styles, the author's last name is followed by their initials. However, some styles may require the full first name or a combination of the first initial and full last name.

How do you format author names for single authors?

To format author names for single authors:

• Write the author's last name first, followed by a comma
• Include the author's initials or full first name, depending on the citation style
• Separate the initials or first name from the last name with a comma

Step 5: Format Author Names for Multiple Authors

When there are multiple authors, the format for citing their names will vary depending on the citation style. Some styles require listing all authors' names, while others only require listing the first author's name followed by "et al." It is important to follow the specific guidelines of the citation style you are using.

How do you format author names for multiple authors?

To format author names for multiple authors:

• List all authors' names if required by the citation style
• Use the word "and" to separate the last two authors' names
• Use a comma to separate the other authors' names
• Use "et al." after the first author's name if required by the citation style

Step 6: Include Author Names in In-text Citations

In-text citations are used to acknowledge the author's work within the body of your research paper. When including author names in in-text citations, it is important to follow the specific rules of the citation style you are using. In most cases, the author's last name and the year of publication are included in parentheses.

How do you include author names in in-text citations?

To include author names in in-text citations:

• Place the author's last name and the year of publication in parentheses
• Separate the last name and the year with a comma
• Place the in-text citation at the end of the sentence or paragraph

Step 7: Include Author Names in the Reference List

The reference list is a separate section at the end of your research paper that provides detailed information about the sources you have cited. Including author names in the reference list follows a specific format based on the citation style you are using. It typically includes the author's last name, initials, or full first name, depending on the style.

How do you include author names in the reference list?

To include author names in the reference list:

• Follow the author's name with the year of publication in parentheses
• Include the title of the research paper, the name of the journal or book, and other relevant publication information

Step 8: Use Proper Punctuation and Capitalization

When writing author names in your research paper, it is important to use proper punctuation and capitalization. Different citation styles have specific rules regarding the use of commas, periods, and capital letters . Pay attention to these details to ensure accurate and consistent formatting.

What are the rules for punctuation and capitalization?

The rules for punctuation and capitalization may vary depending on the citation style. Some general guidelines include:

• Use a comma to separate the author's last name from their initials or first name
• Use a period after each initial in the author's name
• Capitalize the first letter of the author's last name and any proper nouns
• Use title case for the title of the research paper or book

Step 9: Proofread and Edit Your Citations

After completing the previous steps, it is crucial to proofread and edit your citations for accuracy and consistency. Check for any errors in spelling, punctuation, or formatting. Make sure that all author names are correctly cited and that they match the information provided in the original source.

How do you proofread and edit your citations?

To proofread and edit your citations:

• Check for spelling errors in the author's name
• Ensure that the punctuation and capitalization are consistent
• Verify that the citation style guidelines have been followed correctly
• Compare the citations with the original source to ensure accuracy

By following these ten easy steps, you can confidently write author names in your research paper. Remember to consult the appropriate citation style guide for specific formatting rules. Properly citing author names not only demonstrates your academic integrity but also enhances the credibility of your research.

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How should I write the author's name in a research paper?

In a research paper, the author's name should be written in the format: Last name, First name. If there are multiple authors, separate their names with commas.

What if the author has a middle name or initial?

If the author has a middle name or initial, include it after the first name. For example: Last name, First name Middle initial.

Should I include the author's credentials or titles?

In general, it is not necessary to include the author's credentials or titles in a research paper. However, if the author has a specific title or credential that is relevant to the research, it can be included after the name.

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How To Handle Author Names in APA Style

Posted by Rene Tetzner | May 29, 2021 | Referencing & Bibliographies | 0 |

How To Handle Author Names in APA Style Although the documentation style of the American Psychological Association (APA) is widely used for research papers in the social sciences and other fields of study, it is far from the easiest of referencing systems to use effectively. Citing author names correctly in APA style can be especially tricky, so this article outlines with examples exactly how author names should be handled in both in-text citations and complete bibliographical references according to the sixth edition of the APA’s Publication Manual.

For in-text citations, usually only the surname of the author is required along with the date of the publication. This name can be given in the main text or in parentheses, as in ‘Smith (2013)’ or ‘(Smith, 2013).’ If two works published in the same year and written by different authors who share the same surname are cited, initials will be necessary to avoid confusion: ‘(M. Smith, 2013),’ for instance, versus ‘(O. Smith, 2013).’ A work with two authors is cited in the same way as a work with one: ‘Smith and Jones (2014)’ or ‘(Smith & Jones, 2014).’ Notice that an ampersand (&) replaces the word ‘and’ before the last name when the author names are given in parentheses.

When a document has three, four or five authors, all the surnames should be recorded when the source is first cited, but for subsequent citations of the same work, only the first author’s name along with ‘et al.’ is used. The first citation of a work with four authors would therefore take one of these forms: ‘Smith, Jones, Wilson and Johnson (2015)’ or ‘(Smith, Jones, Wilson & Johnson, 2015).’ All subsequent citations of the same source should appear thus: ‘Smith et al. (2015)’ or ‘(Smith et al., 2015).’ Further information is only necessary for subsequent citations if two references published in the same year shorten to exactly the same form. A reference to a 2015 study by Smith, Jones, Wilson and Johnson, for instance, would shorten to the same form as would a reference to a 2015 study by Smith, Jones, Ashfield, Wilson and Greenway, so as many author names as necessary to clarify which work is intended must be added: ‘Smith, Jones, Wilson, et al. (2015)’ for the one and ‘Smith, Jones, Ashfield, et al. (2015)’ for the other.

For in-text citations of a source with six or more authors, the first author’s name is provided along with ‘et al.’ for all citations: A work by Smith, Jones, Wilson, Johnson, Ashfield and Greenway would therefore be cited either as ‘Smith et al. (2016)’ or ‘(Smith et al., 2016).’ Again, additional names will be necessary when two or more references shorten to the same form, so if a 2016 article by Smith, Jones, Ashfield, Wilson, Johnson and Greenway were also cited, three author names would be necessary in each case to distinguish the two sources: ‘Smith, Jones, Wilson, et al. (2016)’ for the first and ‘Smith, Jones, Ashfield, et al. (2016)’ for the second.

In the list of references that appears at the end of a research document using APA style, initials are included along with surnames. The initials of each author should be placed after the author’s surname, as in ‘Smith, M., Jones, W., Wilson, S., & Johnson, N.’ This format is appropriate for up to seven authors; for works with eight or more authors, only the first six authors are listed, followed by an ellipsis (represented by three stops) and the name of the last author. This means that the author names for an article by Smith, Jones, Ashfield, Wilson, Johnson, Neilson, Rayburn and Greenway would take this form: ‘Smith, M., Jones, W., Ashfield, B., Wilson, S., Johnson, N., Neilson, R., . . . Greenway, P.’ To distinguish authors who share the same surnames and initials, first names can be added in square brackets, as in ‘Smith, M. [Mark]’ and ‘Smith, M. [Matthew].’

Each reference in an APA list of references should begin with the surname of the primary author, and the list is arranged alphabetically on the basis of these surnames. For works by the same author (or authors) the references should follow the chronological order of the publications, with ‘Smith, M. (2013)’ preceding ‘Smith, M. (2014).’ Works by the same primary author but with different additional authors should be ordered alphabetically based on the names that differ, so ‘Smith, M., & Jones, W.’ comes before ‘Smith, M., & Wilson, S.’ regardless of date of publication. Alphabetical order for APA references should generally be determined letter by letter, so ‘Smith, A.’ precedes ‘Smith, B.’ and ‘Mac’ precedes ‘Mc,’ but the principle of nothing preceding something also applies, meaning that ‘Smith, M.’ always comes before ‘Smith, M., & Jones, W.’ and ‘Johns, S.’ comes before ‘Johnson, N.’ even though ‘o’ precedes ‘S’ alphabetically speaking.

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How To Handle Author Names Correctly in APA Style

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APA 7th Edition Style Guide: Authors

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General Rules for Authors in References

Personal Authors (9.7-9.12)

List author names in the order they appear in the document or text. Begin with the surname followed by the initials of the first and middle name. Place a comma after the surname. Place a period after each initial in the first and middle name. Separate names with a comma and space. When a work has more than one author put an ampersand (&) before the last surname. End with a period.

Jones, B.R., Van deWater, M.L., Anderson, K., & MacMillan, J.S.

Morgan, J.R. & Tellescue, A. (Eds.).

de Wollen, S.

2 to 20 authors : List up to 20 author surnames and initials separated by commas. List surnames as they appear in the document.

21 or more authors : List the first 19  then add three ellipses and the last author's name.

Carrey, J. M., Riley, P., James, K. K., Edgerton, R., Smith, T. A., Rowland, P., Perry, T. H., Shimoni, R. D., Dion, C., Tignor, M, Auberry, K., Carlson, A., Williams, B., Johnson, S. Kirkman, L. J., Vista, D. D., Barry, B., Austen, J., Andover, S. S....Henry, J.D.

Organizational Authors (9.11)

Organizations or groups as author : Spell out the full name of the group or organization even if a well-known acronym exists. Use the most specific agency as the author when citing a government source that has more than one division.

Central Intelligence Agency.

Substance Abuse and Mental Health Services Administration.

Secondary Authors

Personal author and editor:   If a work has an editor in addition to a personal or organizational author, include both. The editor(s) are placed before the title statement and the name is not inverted.

Luzikov, V.N.. (1985). In D.B. Roodyn (Ed.), Mitochondrial biogenesis and breakdown .

Editor as author: If a work has no personal or organizational author, but has an editor, the secondary author is a required component of a reference (placed in the author position and invert the names).

Leonard, W.R. & Crawford, M.H. (Eds).

No author: If a work has no identifiable author (personal, organization, or editor), begin the reference with the title. Only use the term  Anonymous  for an author when it is given.

Manneristical. (n.d.). Collins English dictionary: Complete & unabridged (10th ed.).  http://dictionary.reference.com/browse/manneristical

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Frequently asked questions

How do i write author names in an ama reference.

On your AMA reference page , author names are written with the last name first, followed by the initial(s) of their first name and middle name if mentioned.

There’s a space between the last name and the initials, but no space or punctuation between the initials themselves. The names of multiple authors are separated by commas , and the whole list ends in a period, e.g., “Andreessen F, Smith PW, Gonzalez E.”

Frequently asked questions: AMA

In AMA citation format , if you cite the same source more than once in your paper, it still only has one entry on your AMA reference page , numbered based on the first time you cite it.

This means you’ll always use the same number for the AMA in-text citation for that source, not a different number each time. You can add different page numbers to the citations to talk about specific parts of the source in each case, e.g. 1 (pp13–15)

An AMA in-text citation just consists of the number of the relevant entry on your AMA reference page , written in superscript at the point in the text where the source is referred to.

You don’t need to mention the author of the source in your sentence, but you can do so if you want. It’s not an official part of the citation, but it can be useful as part of a signal phrase introducing the source.

The names of up to six authors should be listed for each source on your AMA reference page , separated by commas . For a source with seven or more authors, you should list the first three followed by “ et al ” : “Isidore, Gilbert, Gunvor, et al.”

In the text, mentioning author names is optional (as they aren’t an official part of AMA in-text citations ). If you do mention them, though, you should use the first author’s name followed by “et al” when there are three or more : “Isidore et al argue that …”

Note that according to AMA’s rather minimalistic punctuation guidelines, there’s no period after “et al” unless it appears at the end of a sentence. This is different from most other styles, where there is normally a period.

Yes, you should normally include an access date in an AMA website citation (or when citing any source with a URL). This is because webpages can change their content over time, so it’s useful for the reader to know when you accessed the page.

When a publication or update date is provided on the page, you should include it in addition to the access date. The access date appears second in this case, e.g., “Published June 19, 2021. Accessed August 29, 2022.”

Don’t include an access date when citing a source with a DOI (such as in an AMA journal article citation ).

An AMA reference usually includes the author’s last name and initials, the title of the source, information about the publisher or the publication it’s contained in, and the publication date. The specific details included, and the formatting, depend on the source type.

References in AMA style are presented in numerical order (numbered by the order in which they were first cited in the text) on your reference page. A source that’s cited repeatedly in the text still only appears once on the reference page.

An AMA in-text citation consists of the number of the relevant reference on your AMA reference page , written in superscript 1 at the point in the text where the source is used.

It may also include the page number or range of the relevant material in the source (e.g., the part you quoted 2(p46) ). Multiple sources can be cited at one point, presented as a range or list (with no spaces 3,5–9 ).

AMA citation format is a citation style designed by the American Medical Association. It’s frequently used in the field of medicine.

You may be told to use AMA style for your student papers. You will also have to follow this style if you’re submitting a paper to a journal published by the AMA.

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Research Impact : Establishing Your Author Name and Presence

• Outputs and Activities
• Establishing Your Author Name and Presence
• Enhancing Your Impact
• Tracking Your Work
• Telling Your Story
• Impact Frameworks

Why is Establishing Your Author Profile Important?

Your name as an author is key to establishing a unique public profile for enhancing your research and for attribution purposes. Authors should use the same version of their name consistently throughout their academic and research careers.

Establishing Your Author Profiles

How can authors find out if other authors have similar names? One tip is to do an author search in several databases such as PubMed/MEDLINE, Scopus or Web of Science .

• How many name variants are there for your name?
• How many authors share your name?
• How many authors with a similar name have publications in the same subject area?
• Is it possible to distinguish publications from authors with similar names? 

If you find similarities in author names, consider adding your full middle name or using your middle initial to distinguish it from other authors.

There are several resources to help authors manage unique and consistent author names to ensure that their publications are associated with the correct author.

We highly recommend that authors create an ORCID ID, and check their profiles in Scopus, Web of Science and Google Scholar. An ORCID ID can be created using publication data from Scopus or Web of Science/Researcher ID. Authors may also want to consider creating a LinkedIn or Doximity profile. WUSM physicians are highly encouraged to claim their Doximity profiles.

LinkedIn is a social/professional networking website for people in professional occupations, with privacy settings available. It is highly recommended that investigators or clinicians create a LinkedIn profile. Features of LinkedIn include:

• Helpful resource for recruiting or job-seeking purposes.
• Option to create a custom URL containing your name to your LinkedIn profile.

Doximity is a professional networking site for U.S. physicians, medical students and clinically licensed healthcare professionals. As of 2022, at around 80% of physicians and 50% of NPs and PAs are verified members. Features of Doximity include:

• Profiles are viewable and searchable to allow you to connect with colleagues and classmates.
• Upload your CV to allow for opportunities for networking or job-seeking purposes (or you can send your CV to Doximity, [email protected], to upload for you).
• Add clinical specialties and interests, and information about other profiles.
• Research and compare residency programs.

WUSM physicians are strongly encouraged to claim their Doximity profiles. Why?

• Vote in the U.S. News & World Report Best Hospitals survey .
• Complete the Doximity Residency Navigator.
• Listed in the U.S. News & World Report Doctor Finder tool.

NCBI My Bibliography

The National Center for Biotechnology Information (NCBI) advances science and health by providing access to biomedical and genomic information. Among the resources included are PubMed/MEDLINE, PubMed Central, genomic tools, registries, databases, among others. My NCBI is a dashboard that retains user information and database preferences to provide customized services for NCBI databases/resources. My Bibliography is one of the many tools offered via the My NCBI dashboard page. As of 2010, investigators/authors (funded by NIH or planning to seek funding) are required to use a My Bibliography account to manage their citations to publications and other research products.  Citations to journal articles indexed by PubMed can be pulled into a My Bibliography collection and templates are available for all other publication types and research products.

Examples of citations to publications and other work products that can be included in My Bibliography are:

• Journal articles from PubMed
• Non-PubMed journal articles
• Books/chapters
• Meeting abstracts and posters
• Dataset or database
• Presentations
• Interim Research Products

One of the features of My Bibliography is a URL link that allows for linking to the list of publications and research products noted in a My Bibliography collection. The link to the list of publications in a My Bibliography collection mirrors a list of results in PubMed and if users are affiliated with an institution that has a subscription to the journal or if the work is in PubMed Central, users can read the full text of the work. The collection is dynamically updated when investigators/authors (or their delegates) add new citations to their publications and other research products.

Google Scholar

Authors are highly recommended to establish a Google Scholar profile.

Google Scholar  allows authors to:

Create a public profile that appears in Google Scholar results when someone searches for your name. Privacy settings for the Google Scholar profile are controlled by the individual.

Track citations to check who is citing your publications, especially gray literature materials which are not usually indexed by databases.

Citation metric tools to use for reporting purposes.

Set up your profile in Google Scholar

How to keep your Google Scholar Profile clean?

The NIH, AHRQ, and CDC have recently announced that individuals supported by research training, fellowship, research education, and career development awards will be required to have ORCID iDs beginning in FY 2020.​ Read the full notice here .

ORCID provides a persistent digital identifier that distinguishes authors from other authors and, through integration in key research workflows such as manuscript and grant submission, supports automated linkages between authors and their professional activities ensuring that their work is recognized.

ORCID is linked among other identifier systems such as the Scopus Author ID, ResearcherID and LinkedIn; publishers such as Nature and APS; and funding agencies such as NIH and the Wellcome Trust (see the SciENCV tab for more information about ORCID integration with NIH). This means that ORCID is not limited to a specific platform and is a non-proprietary means of establishing your author name.

Your name is key to establishing a unique public profile throughout your research and academic career for publications and research activities. But if your name is a common name or if you have changed your name, or if you are affiliated with several organizations over your career, there may be multiple name variants associated with your publications and research activities.

Registering for an ORCID identifier helps to promote discoverability among multiple information platforms and workflows as well as establishing a unique presence for researchers and scholars, regardless of name variants or affiliation history.

Registration for the ORCID iD is free and privacy settings are controlled by the individual. To register, complete a short registration form and select Register.

See the ORCID materials for more information:

• My NCBI – ORCID Author Data Integration with SciENcv

The Delegate Feature

ORCID has a delegate feature available to help with managing ORCID accounts. "Trusted Individuals" can be added as delegates to an ORCID account to allow for editing and updating of an ORCID account and profile. Note that Trusted Individuals must register for an ORCID account. 

More information:

• Delegating Control to a Trusted Individual

F or more information, see the ORCID Guide .

Scopus Author Identifier

The Author Identifier Tool in Scopus allows users to locate a particular author by entering the author’s last name, full first name and a middle initial, as well as the current affiliation of the author. Results will return a main author name along with variants of the author's name that have been grouped into an author profile and associated publications for that author. The Scopus database addresses the issue of author ambiguation and reconciles authors who use different variations of their names throughout their careers. Authors are highly recommended to review their profile in Scopus to confirm the profile is correct, and set up alerts for their works. Scopus is ORCID compliant allowing users to associate publications from their Scopus Author Profiles to ORCID profiles.

Importing Your Works from Scopus to ORCID Users can use Scopus to populate the publication section of the ORCID profile. The Scopus to ORCID wizard helps you find the correct Scopus profile and confirm which publications are yours. You can then send the identifier and list of publications to the ORCID website. Any changes you make in the wizard will also be submitted to the Scopus Feedback team to correct your profile on Scopus.

• Create an ORCID iD
• Click on “Import Research Activities"
• Choose the Scopus to ORCID wizard to start importing publications

For users that do not have access to the Scopus database, the Author Identifer Tool can be used by non-subscribers. Use the free Author Identifier Tool to search for an author name to reconcile name variants and/or affiliations and publications.

ResearcherID and Publons

ResearcherID provides a solution to the author ambiguity problem within the scholarly research community. Each member is assigned a unique identifier to enable researchers to manage their publication lists, track their times cited counts and h-index, identify potential collaborators and avoid author variant issues. Privacy settings for the ResearcherID profile are controlled by the individual and authors are highly recommended to make their ResearcherID profile publicly available.

ResearcherID information is integrated with the Web of Science database and is ORCID compliant, allowing users to associate publications from Web of Science to ORCID profiles. ResearcherID is also integrated with Publons which is used to track your journal peer review and editing activities along with publications and citation metrics.

Registration for ResearcherID is free.

ResearcherID members are able to register and link to ORCID and Publons from their ResearcherID Profile.

Another means of establishing your name and "presence" is to make your NIH Biosketch publicly available for others to view. NCBI offers a tool, SciENcv , that allows for creation of a NIH Biosketch that can be made publicly available. See the SciENcv tab for more information.

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Guide Credit

This APA 7 guide is based with permission on the APA Style: New 7th Edition guide from Northwest Technical College.

Authors' Names: In-Text Citations (APA 7th ed.)

Two authors

• If the authors' names are in your sentence, use and between their last names.
• If the authors' names are in your parenthetical citation, use & between their last names.
• Add the year and page numbers (if there are any). 
• A recent study examined the impact of citations in medical literature (Pence & Chapman, 2017).
• Pence and Chapman (2017) wrote a definitive work on citation style.

Three or more authors

• Include the name of only the first author plus “et al.” in every citation. Add the year and page numbers (if there are any). For example: (Emerson et al., 1993, p. 76)
• Include the title and year of publication. If the title is long (more than 3 words), shorten it.
• If the title of the work is not italicized in the reference (article, book chapter, page or section on a website), use the first word or two of the title in quotation marks.
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• For example, if you had an article with the title Practical oral care for people with intellectual disability, the parenthetical citation would look like ("Practical oral," 2014).

Authors' Names: References Page (APA 7th ed.)

Two to 20 authors: .

List by their last names and initials, separated by a comma. Put an  &  between the final two names.

Brooks, K., & Dunn, R.                        

Knowles-Carter, B., Carter, B.I., & Carter, S.

21 or more authors: 

List by last names and initials; commas separate author names. 

Include the first 19 authors’ names, insert an ellipsis ... (but no ampersand), and then add the final author’s name:.

Author, A. A., Author, B. B., Author, C. C., Author, D. D., Author, E. E., Author, F. F., Author, G. G., Author, H. H., Author, I. I., Author, J. J., Author, K. K., Author, L. L., Author, M. M., Author, N. N., Author, O. O., Author, P. P., Author, Q. Q., Author, R. R., Author, S. S., . . . Author, Z. Z.. (2018). 

Skip authors' names and start your citation with the title.

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If you can't find an author affiliation or want to learn more about the authors and their credentials, here are some ways to do so:

• Search for the author on Google. Sometimes you can find a personal page about an individual. Many of the faculty members at OSU have a website that lists their credentials (education) and research.
• Do a search in one of the online databases to see what else the author has written. Is this person someone who published a lot in this field? For example, a search in the Academic Search Complete database for the author Sandra Hofferth shows the articles she has co-authored on a range of children's issues .
• Look up the institution. What kind of institution is it?  Is the author still affiliated with the institution?

One of the first things to look for is the author or authors. In a research article, the authors will list their affiliation, usually with a university or research institution. In this example, the author's affiliation is clearly shown on the first page of the article. In a research article, you will never have an anonymous author or need to look for the author's name or affiliation.

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Referencing guide: works cited - author.

• AGLC This link opens in a new window
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• Transition from Harvard to APA
• General principles
• In-text citations
• Works Cited

Works Cited - Author

• Works Cited - Title
• Works cited - Title of Container
• Works Cited - Other Contributors
• Works Cited - Version & Number
• Works Cited - Publisher & Publication Date
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Author . Title of source .  Title of container , Other contributors, Version, Number, Publisher, Publication date, Location.

Start with the author’s last name, followed by a comma, then the rest of the name – exactly as presented in the work itself. End this element with a full stop.

The "author" is usually understood to be the person/s or an organisation primarily responsible for the creation of the work. However, sometimes your focus may be on a particular aspect of the work , e.g. a particular translation. In that case, the translator's name is used as the "author". Follow the name with the label describing the role, e.g. Brown, John, translator.

Single author

Use the author’s last name, followed by a comma, then the rest of the name – exactly as presented in the work itself. End this element with a full stop, e.g.

Two authors

The authors’ names should be listed in the same order as in the source. Start with the first author’s last name, followed by a comma, then the rest of the name. This is followed by a comma and the word  and.  Then write the second author’s name in the normal order, i.e. first name/s and surname, e.g.

Hill, John, and Pamela Church Gibson , editors. The Oxford Guide to Film Studies . Oxford UP, 1998, pp. 35-36.

Three or more authors

Start with the first author’s last name, followed by a comma, then the rest of the name, followed by a comma and et al. , e.g.

Royle , Jo, et al. "The Use of Branding by Trade Publishers: An Investigation into Marketing the Book as a Brand Name Product." Publishing Research Quarterly , vol. 15, no. 4, 1999, pp. 3-13, doi: doi.org/10.1007/s12109-999-0031-1.

Corporate author

If the author of the source is an organisation, as opposed to a person, use the organisation’s name as the author. List all the administrative units identified in the work, separated by commas, e.g.

State of Tasmania, Department of State Growth. Tasmanian Global Education Growth Strategy . May 2017, p. 14, www.stategrowth.tas.gov.au/__data/assets/pdf_file/0008/149804/Global_Education_Strategy_for_web.pdf.

If the author of the work is the organisation that also published it, start the entry with the Title of the work and list the organisation only as the publisher.

Who Wrote the Movie and What Else Did He Write? : An Index of Screen Writers and Their Film Works, 1936-1969 . Academy of Motion Picture Arts and Sciences, 1970, p. 78.

Pseudonyms, online usernames, etc

Pseudonyms, e.g. George Eliot would be Eliot, George, online usernames, e.g. @realDonaldTrump, etc, are treated like standard names.  Special characters such as @ are ignored for the purpose of alphabetising the entry in the list of Works Cited.

If the work is published without the author's name start the entry with the Title of the source. Do not list the author as "Anonymous”, e.g.

The Arabian Nights. Bloomsbury, 1994. Children's Classics.

Style Manual

If you cannot find an example for what you are looking for here, consult the MLA website , or the MLA Handbook (below)  

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13.1 Formatting a Research Paper

Learning objectives.

• Identify the major components of a research paper written using American Psychological Association (APA) style.
• Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

• AMA (American Medical Association) for medicine, health, and biological sciences
• APA (American Psychological Association) for education, psychology, and the social sciences
• Chicago—a common style used in everyday publications like magazines, newspapers, and books
• MLA (Modern Language Association) for English, literature, arts, and humanities
• Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

• Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
• Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
• Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

• Headings and, if necessary, subheadings to organize the content
• In-text citations of research sources
• References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

• Title of the paper
• Author’s name
• Name of the institution with which the author is affiliated
• Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

• Set the top, bottom, and side margins of your paper at 1 inch.
• Use double-spaced text throughout your paper.
• Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
• Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
• Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

• Your title page
• The abstract you created in Note 13.8 “Exercise 1”
• Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

• Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
• Subsection headings use left-aligned, boldface type. Headings use title case.
• The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
• The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
• The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

• MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
• Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
• Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

• The name(s) of the author(s) or institution that wrote the source
• The year of publication and, where applicable, the exact date of publication
• The full title of the source
• For books, the city of publication
• For articles or essays, the name of the periodical or book in which the article or essay appears
• For magazine and journal articles, the volume number, issue number, and pages where the article appears
• For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

• Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
• Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
• APA papers usually include an abstract that concisely summarizes the paper.
• APA papers use a specific headings structure to provide a clear hierarchy of information.
• In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
• In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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K.J.V. is affiliated with bar.on. The other authors declare no competing interests.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Some of the most valuable lessons come from outside the classroom. So, immerse yourself in these books and complement your college education with insights and wisdom that will last a lifetime.